Group Analysis by Visualized Distributional Representation for Resting-State Functional Brain Connectivity

Jiating Zhu, Jiannong Cao

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)


The functional brain connectivity based on resting-sate functional magnetic resonance imaging (fMRI) data has been widely studied. However, most of the analyzes, based on functional brain network construction, either limit themselves to descriptors from graph-theoretical measures and subgraph patterns, or fail to gain the whole picture of the large-scale functional brain network. Due to the curse of the dimensionality, the millions-dimension data of the correlation information from fMRIs can not be directly processed by the current machine learning method, even with the state-of-art deep learning architecture. Hence, a good representation that can be both informative and compressed is strongly desired. In this paper, we introduce a distributional representation that can provide information across high resolution regions to low resolution regions in a brain. It is believed that a good representation of functional brain connectivity can easily separate patients with brain disorders from normal controls. In our case, the difference between groups is visualized clearly with the distributional representation. The visualized group difference not only gives a visible explanation for existing techniques in functional brain network construction, but also provides extra clues for the informativeness of weak connections, which are neglected in traditional analysis. Moreover, we propose an approach with distributional representation mappings to validate the effectiveness of the features visualized in the distributional representation. We find that the mapped distributional representation improves the performance dramatically in distinguishing subjects with a given mental disorder from the control ones. The results suggests that the distributional representation is promising for further design of algorithms, such as clustering and classification, for brain disorders.

Original languageEnglish
Title of host publicationProceedings - 2018 14th International Conference on Semantics, Knowledge and Grids, SKG 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781728104416
Publication statusPublished - 2 Jul 2018
Event14th International Conference on Semantics, Knowledge and Grids, SKG 2018 - Guangzhou, China
Duration: 12 Sep 201814 Sep 2018

Publication series

NameProceedings - 2018 14th International Conference on Semantics, Knowledge and Grids, SKG 2018


Conference14th International Conference on Semantics, Knowledge and Grids, SKG 2018

ASJC Scopus subject areas

  • Computer Networks and Communications

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